Complex network community mining method based on cellular automatic learning machine

A cellular automatic and complex network technology, applied in the field of complex network community mining, can solve problems including unreasonable communities, increased method complexity, and ignoring the tightness of local community structures

Inactive Publication Date: 2014-11-19
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

This scheme uses the local optimal solution search, which greatly improves the detection accuracy, but also increases the complexity of the method; at the same time, because the closeness of the local community structure is ignored, the obtained communities often contain unreasonable communities.

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  • Complex network community mining method based on cellular automatic learning machine
  • Complex network community mining method based on cellular automatic learning machine
  • Complex network community mining method based on cellular automatic learning machine

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Embodiment Construction

[0065] The following is a detailed description of the embodiments of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0066] This embodiment adopts the classic data set Zachary karate club network in the social network, the network contains 34 nodes and 78 edges, and the network topology is as follows figure 2 shown. The specific steps are as follows:

[0067] 1. Initialize the cellular automatic learning machine. For the karate club network, construct its adjacency matrix A, and assign an automatic learning machine L to each node i in the network i . All automatic learning machines in the network are connected through edges in the network to form a cellular automatic learning machine U. Initialize each automati...

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Abstract

The invention discloses a complex network community mining method based on a cellular automatic learning machine. The method comprises the six steps of initializing the cellular automatic learning machine, generating state vectors of the cellular automatic learning machine, decoding the state vectors of the cellular automatic learning machine to obtain the corresponding communities, calculating response signals, updating the cellular automatic learning machine, and comparing community structures. According to the complex network community mining method, the whole network is modeled into the cellular automatic learning machine, the communities in the network are mapped into the state vectors of the cellular automatic learning machine, and the network is searched for the optimal community structure through iterative updating of the cellular automatic learning machine. The complex network community mining method is quite low in time complexity and applicable to a large-scale complex network. In addition, the complex network community mining method has the good performance for searching for the globally optimal solution, and can ensure compactness of local communities, so that community detection precision is quite high.

Description

technical field [0001] The invention relates to the technical field of complex network community mining, in particular to a complex network community mining method based on a cellular automatic learning machine. Background technique [0002] A complex network is an abstract representation of complex systems in the real world. The nodes in the network represent the individuals in the system, and the connections between nodes represent the interrelationships between the individuals in the system. At present, complex networks have been widely used to characterize various complex systems such as power networks, communication networks, Internet networks, social networks, neural networks, and protein networks. [0003] Community structure is an important topological characteristic of complex networks. The entire complex network is composed of several communities. The nodes within each community are very closely connected, while the connections between communities are relatively sp...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/958
Inventor 李生红张爱新赵成林李建华赵郁忻
Owner SHANGHAI JIAO TONG UNIV
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